clippy: adhere to pedantic clippy, uniform test error handling

This commit is contained in:
Per Stark
2026-05-26 11:43:45 +02:00
parent 6a5d631287
commit 000852c94c
68 changed files with 2468 additions and 2547 deletions
+163 -154
View File
@@ -11,6 +11,7 @@ use axum::{
use common::storage::types::{
serde_helpers::deserialize_flexible_id,
text_content::TextContent,
user::User,
StoredObject,
};
use retrieval_pipeline::{RetrievalConfig, SearchResult, SearchTarget, StrategyOutput};
@@ -46,13 +47,11 @@ fn source_id_suffix(source_id: &str) -> String {
fn truncate_label(value: &str, max_chars: usize) -> String {
let mut end = None;
let mut count = 0;
for (idx, _) in value.char_indices() {
for (count, (idx, _)) in value.char_indices().enumerate() {
if count == max_chars {
end = Some(idx);
break;
}
count += 1;
}
match end {
@@ -174,165 +173,31 @@ struct KnowledgeEntityForTemplate {
score: f32,
}
#[derive(Serialize)]
struct SearchResultForTemplate {
result_type: String,
score: f32,
#[serde(skip_serializing_if = "Option::is_none")]
text_chunk: Option<TextChunkForTemplate>,
#[serde(skip_serializing_if = "Option::is_none")]
knowledge_entity: Option<KnowledgeEntityForTemplate>,
}
#[derive(Serialize)]
pub struct AnswerData {
search_result: Vec<SearchResultForTemplate>,
query_param: String,
}
pub async fn search_result_handler(
State(state): State<HtmlState>,
Query(params): Query<SearchParams>,
RequireUser(user): RequireUser,
) -> Result<impl IntoResponse, HtmlError> {
#[derive(Serialize)]
struct SearchResultForTemplate {
result_type: String,
score: f32,
#[serde(skip_serializing_if = "Option::is_none")]
text_chunk: Option<TextChunkForTemplate>,
#[serde(skip_serializing_if = "Option::is_none")]
knowledge_entity: Option<KnowledgeEntityForTemplate>,
}
#[derive(Serialize)]
pub struct AnswerData {
search_result: Vec<SearchResultForTemplate>,
query_param: String,
}
let (search_results_for_template, final_query_param_for_template) = if let Some(actual_query) =
params.query
{
let trimmed_query = actual_query.trim();
if trimmed_query.is_empty() {
(Vec::<SearchResultForTemplate>::new(), String::new())
} else {
// Use retrieval pipeline Search strategy
let config = RetrievalConfig::for_search(SearchTarget::Both);
// Checkout a reranker lease if pool is available
let reranker_lease = match &state.reranker_pool {
Some(pool) => Some(pool.checkout().await),
None => None,
};
let result = retrieval_pipeline::pipeline::run_pipeline(
&state.db,
&state.openai_client,
Some(&state.embedding_provider),
trimmed_query,
&user.id,
config,
reranker_lease,
)
.await?;
let search_result = match result {
StrategyOutput::Search(sr) => sr,
_ => SearchResult::new(vec![], vec![]),
};
let mut source_ids = HashSet::new();
for chunk_result in &search_result.chunks {
source_ids.insert(chunk_result.chunk.source_id.clone());
}
for entity_result in &search_result.entities {
source_ids.insert(entity_result.entity.source_id.clone());
}
let source_label_map = if source_ids.is_empty() {
HashMap::new()
} else {
let record_ids: Vec<RecordId> = source_ids
.iter()
.filter_map(|id| {
if id.contains(':') {
RecordId::from_str(id).ok()
} else {
Some(RecordId::from_table_key(TextContent::table_name(), id))
}
})
.collect();
let mut response = state
.db
.client
.query(
"SELECT id, url_info, file_info, context, category, text FROM type::table($table_name) WHERE user_id = $user_id AND id INSIDE $record_ids",
)
.bind(("table_name", TextContent::table_name()))
.bind(("user_id", user.id.clone()))
.bind(("record_ids", record_ids))
.await?;
let contents: Vec<SourceLabelRow> = response.take(0)?;
tracing::debug!(
source_id_count = source_ids.len(),
label_row_count = contents.len(),
"Resolved search source labels"
);
let mut labels = HashMap::new();
for content in contents {
let label = build_source_label(&content);
labels.insert(content.id.clone(), label.clone());
labels.insert(
format!("{}:{}", TextContent::table_name(), content.id),
label,
);
}
labels
};
let mut combined_results: Vec<SearchResultForTemplate> =
Vec::with_capacity(search_result.chunks.len() + search_result.entities.len());
// Add chunk results
for chunk_result in search_result.chunks {
let source_label = source_label_map
.get(&chunk_result.chunk.source_id)
.cloned()
.unwrap_or_else(|| fallback_source_label(&chunk_result.chunk.source_id));
combined_results.push(SearchResultForTemplate {
result_type: "text_chunk".to_string(),
score: chunk_result.score,
text_chunk: Some(TextChunkForTemplate {
id: chunk_result.chunk.id,
source_id: chunk_result.chunk.source_id,
source_label,
chunk: chunk_result.chunk.chunk,
score: chunk_result.score,
}),
knowledge_entity: None,
});
}
// Add entity results
for entity_result in search_result.entities {
let source_label = source_label_map
.get(&entity_result.entity.source_id)
.cloned()
.unwrap_or_else(|| fallback_source_label(&entity_result.entity.source_id));
combined_results.push(SearchResultForTemplate {
result_type: "knowledge_entity".to_string(),
score: entity_result.score,
text_chunk: None,
knowledge_entity: Some(KnowledgeEntityForTemplate {
id: entity_result.entity.id,
name: entity_result.entity.name,
description: entity_result.entity.description,
entity_type: format!("{:?}", entity_result.entity.entity_type),
source_id: entity_result.entity.source_id,
source_label,
score: entity_result.score,
}),
});
}
// Sort by score descending
combined_results.sort_by(|a, b| b.score.total_cmp(&a.score));
// Limit results
const TOTAL_LIMIT: usize = 10;
combined_results.truncate(TOTAL_LIMIT);
(combined_results, trimmed_query.to_string())
}
perform_search(&state, &user, actual_query).await?
} else {
(Vec::<SearchResultForTemplate>::new(), String::new())
};
@@ -345,3 +210,147 @@ pub async fn search_result_handler(
},
))
}
async fn perform_search(
state: &HtmlState,
user: &User,
query: String,
) -> Result<(Vec<SearchResultForTemplate>, String), HtmlError> {
const TOTAL_LIMIT: usize = 10;
let trimmed_query = query.trim();
if trimmed_query.is_empty() {
return Ok((Vec::new(), String::new()));
}
let config = RetrievalConfig::for_search(SearchTarget::Both);
let reranker_lease = match &state.reranker_pool {
Some(pool) => pool.checkout().await,
None => None,
};
let params = retrieval_pipeline::pipeline::StrategyParams {
db_client: &state.db,
openai_client: &state.openai_client,
embedding_provider: Some(&state.embedding_provider),
input_text: trimmed_query,
user_id: &user.id,
config,
reranker: reranker_lease,
};
let result = retrieval_pipeline::pipeline::execute(params).await?;
let search_result = match result {
StrategyOutput::Search(sr) => sr,
_ => SearchResult::new(vec![], vec![]),
};
let source_label_map = resolve_source_labels(state, user, &search_result).await?;
let mut combined_results: Vec<SearchResultForTemplate> =
Vec::with_capacity(search_result.chunks.len().saturating_add(search_result.entities.len()));
for chunk_result in search_result.chunks {
let source_label = source_label_map
.get(&chunk_result.chunk.source_id)
.cloned()
.unwrap_or_else(|| fallback_source_label(&chunk_result.chunk.source_id));
combined_results.push(SearchResultForTemplate {
result_type: "text_chunk".to_string(),
score: chunk_result.score,
text_chunk: Some(TextChunkForTemplate {
id: chunk_result.chunk.id,
source_id: chunk_result.chunk.source_id,
source_label,
chunk: chunk_result.chunk.chunk,
score: chunk_result.score,
}),
knowledge_entity: None,
});
}
for entity_result in search_result.entities {
let source_label = source_label_map
.get(&entity_result.entity.source_id)
.cloned()
.unwrap_or_else(|| fallback_source_label(&entity_result.entity.source_id));
combined_results.push(SearchResultForTemplate {
result_type: "knowledge_entity".to_string(),
score: entity_result.score,
text_chunk: None,
knowledge_entity: Some(KnowledgeEntityForTemplate {
id: entity_result.entity.id,
name: entity_result.entity.name,
description: entity_result.entity.description,
entity_type: format!("{:?}", entity_result.entity.entity_type),
source_id: entity_result.entity.source_id,
source_label,
score: entity_result.score,
}),
});
}
combined_results.sort_by(|a, b| b.score.total_cmp(&a.score));
combined_results.truncate(TOTAL_LIMIT);
Ok((combined_results, trimmed_query.to_string()))
}
async fn resolve_source_labels(
state: &HtmlState,
user: &User,
search_result: &SearchResult,
) -> Result<HashMap<String, String>, HtmlError> {
let mut source_ids = HashSet::new();
for chunk_result in &search_result.chunks {
source_ids.insert(chunk_result.chunk.source_id.clone());
}
for entity_result in &search_result.entities {
source_ids.insert(entity_result.entity.source_id.clone());
}
if source_ids.is_empty() {
return Ok(HashMap::new());
}
let record_ids: Vec<RecordId> = source_ids
.iter()
.filter_map(|id| {
if id.contains(':') {
RecordId::from_str(id).ok()
} else {
Some(RecordId::from_table_key(TextContent::table_name(), id))
}
})
.collect();
let mut response = state
.db
.client
.query(
"SELECT id, url_info, file_info, context, category, text FROM type::table($table_name) WHERE user_id = $user_id AND id INSIDE $record_ids",
)
.bind(("table_name", TextContent::table_name()))
.bind(("user_id", user.id.clone()))
.bind(("record_ids", record_ids))
.await?;
let contents: Vec<SourceLabelRow> = response.take(0)?;
tracing::debug!(
source_id_count = source_ids.len(),
label_row_count = contents.len(),
"Resolved search source labels"
);
let mut labels = HashMap::new();
for content in contents {
let label = build_source_label(&content);
labels.insert(content.id.clone(), label.clone());
labels.insert(
format!("{}:{}", TextContent::table_name(), content.id),
label,
);
}
Ok(labels)
}